Quantum compiling by deep reinforcement learning

Quantum compilers are characterized by a trade-off between the length of the sequences, the precompilation time, and the execution time. Here, the authors propose an approach based on deep reinforcement learning to approximate unitary operators as circuits, and show that this approach decreases the...

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Autores principales:	Lorenzo Moro, Matteo G. A. Paris, Marcello Restelli, Enrico Prati
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Astrophysics QB460-466 Physics QC1-999
Acceso en línea:	https://doaj.org/article/ab73e5736a1642b98d3c091262945c6e
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spelling	oai:doaj.org-article:ab73e5736a1642b98d3c091262945c6e2021-12-02T18:49:34ZQuantum compiling by deep reinforcement learning10.1038/s42005-021-00684-32399-3650https://doaj.org/article/ab73e5736a1642b98d3c091262945c6e2021-08-01T00:00:00Zhttps://doi.org/10.1038/s42005-021-00684-3https://doaj.org/toc/2399-3650Quantum compilers are characterized by a trade-off between the length of the sequences, the precompilation time, and the execution time. Here, the authors propose an approach based on deep reinforcement learning to approximate unitary operators as circuits, and show that this approach decreases the execution time, potentially allowing real-time quantum compiling.Lorenzo MoroMatteo G. A. ParisMarcello RestelliEnrico PratiNature PortfolioarticleAstrophysicsQB460-466PhysicsQC1-999ENCommunications Physics, Vol 4, Iss 1, Pp 1-8 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Astrophysics QB460-466 Physics QC1-999
spellingShingle	Astrophysics QB460-466 Physics QC1-999 Lorenzo Moro Matteo G. A. Paris Marcello Restelli Enrico Prati Quantum compiling by deep reinforcement learning
description	Quantum compilers are characterized by a trade-off between the length of the sequences, the precompilation time, and the execution time. Here, the authors propose an approach based on deep reinforcement learning to approximate unitary operators as circuits, and show that this approach decreases the execution time, potentially allowing real-time quantum compiling.
format	article
author	Lorenzo Moro Matteo G. A. Paris Marcello Restelli Enrico Prati
author_facet	Lorenzo Moro Matteo G. A. Paris Marcello Restelli Enrico Prati
author_sort	Lorenzo Moro
title	Quantum compiling by deep reinforcement learning
title_short	Quantum compiling by deep reinforcement learning
title_full	Quantum compiling by deep reinforcement learning
title_fullStr	Quantum compiling by deep reinforcement learning
title_full_unstemmed	Quantum compiling by deep reinforcement learning
title_sort	quantum compiling by deep reinforcement learning
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/ab73e5736a1642b98d3c091262945c6e
work_keys_str_mv	AT lorenzomoro quantumcompilingbydeepreinforcementlearning AT matteogaparis quantumcompilingbydeepreinforcementlearning AT marcellorestelli quantumcompilingbydeepreinforcementlearning AT enricoprati quantumcompilingbydeepreinforcementlearning
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Quantum compiling by deep reinforcement learning

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